KwARG

KwARG reconstructs ancestral recombination graphs (ARGs) from aligned genetic sequence datasets using a parsimony-based greedy heuristic to infer recombination and recurrent mutation events.

Key Features:

• Parsimony-Based Greedy Heuristic: Employs a parsimony criterion with a greedy search to minimize the number of recombination and mutation events when reconstructing ARGs.
• Recurrent Mutation Handling: Explicitly accounts for recurrent (parallel or back) mutation events in the reconstruction process.
• Cost Parameter Control: Allows specification of cost parameters to balance the relative penalty between recombination and recurrent mutation events.
• Multiple Candidate Solutions: Outputs a list of alternative candidate ARGs, each describing potential recombination and mutation events explaining the data.
• Computational Efficiency: Uses a heuristic approach intended to improve scalability for larger genetic datasets.

Scientific Applications:

• Population Genetics: Reconstruction of genealogical relationships within samples to study population structure and history.
• Variation Inference: Inference of patterns of genetic variation shaped by recombination and mutation.
• Lineage Tracing: Tracing lineage histories and alternative evolutionary scenarios through candidate ARGs.
• Study of Recombination and Mutation Effects: Analysis of how recombination and recurrent mutation contribute to genetic diversity.

Methodology:

Accepts aligned sequence datasets and applies a parsimony-based greedy heuristic to generate plausible ancestral recombination graphs, supports user-specified cost parameters, and returns multiple candidate solutions describing recombination and mutation events.